mid term review terry a. ring ch en 5253 design ii
TRANSCRIPT
Mid Term Review
Terry A. Ring
CH EN 5253
Design II
Week Day Lecture Topic AssignmentsDue Date
1 12-JanCourse Overview Lecture Review Chapters 7,8,9
14-JanReview of Project EconomicsGeneration of Economics Spread Sheet HW 1
16-JanReview of Project Economics Attainable Rection - HW 2
2 19-JanMLK Holiday
21-JanReview of Reactors - Selectivity & Heat Effects Separation Trains, HW 2 Assigned HW 1
23-JanAttainable Region
3 26-JanSeparations
28-JanDistillation Trains HW-3 Assigned HW 2
30-JanSeparations and Reactors
4 2-FebReactor, Separation and Recycle
4-FebReactor, Separation and Recycle HW-4 Assigned HW 3
6-FebReactor, Separation and Recycle
5 9-FebHeat and Power Integration
11-FebHeat and Power Integration HW-5 Assigned HW 4
13-FebHeat and Power Integration
6 16-FebPresident's Day Holiday
18-FebOptimization on Process Flowsheets HW-6 Assigned HW 5
20-FebEffects of Impurities on Reactors - HX
7 23-FebEffects of Impurites on Separators, Recycle
25-FebPlantwide Control HW-7 Assigned HW 6
27-FebPlantwide Control
8 2-MarReview for Exam
4-MarSequential Batch Processing HW 7
6-MarMid Term Exam Exam
Reactor Heat Effects
S,S&L Chapter 7
Managing Heat Effects
• Reaction Run Away– Exothermic
• Reaction Dies– Endothermic
• Preventing Explosions
• Preventing Stalling
Temperature Effects
• Thermodynamics/Equilibrium
• Kinetics
Unfavorable Equilibrium
• Increasing Temperature Increases the Rate
• Equilibrium Limits Conversion
Reactor with Heating or Cooling
Q = UA ΔT
Best Temperature Path
Optimum Inlet TemperatureExothermic Rxn
Inter-stage Cooler
Exothermic Equilibria
Lowers Temp.
Inter-stage Cold Feed
Exothermic Equilibria
Lowers TempLowers Conversion
Reaction Selectivity
• Parallel Reactions– A+BR (desired)– AS
• Series Reactions– ABC(desired)D
• Independent Reactions– AB (desired)– CD+E
• Series Parallel Reactions– A+BC+D– A+CE(desired)
• Mixing, Temperature and Pressure Effects
Rate Selectivity
• Parallel Reactions– A+BR (desired)– A+BS
• Rate Selectivity
• (αD- αU) >1 make CA as large as possible• (βD –βU)>1 make CB as large as possible
• (kD/kU)= (koD/koU)exp[-(EA-D-EA-U)/(RT)]– EA-D > EA-U T– EA-D < EA-U T
)()(A
U
Drr
D/UD
U
D Ck
kS UDU
BC
Reactor Design to Maximize Desired Product
Maximize Desired Product
• Series Reactions– AB(desired)CD
• Plug Flow Reactor• Optimum Time in Reactor
Real Reaction Systems
• More complicated than either – Series Reactions– Parallel Reactions
• Effects of equilibrium must be considered
• Confounding heat effects
• All have Reactor Design Implications
Engineering Tricks
• Reactor types– Multiple Reactors
• Mixtures of Reactors
– Bypass– Recycle after Separation
• Split Feed Points/ Multiple Feed Points• Diluents• Temperature Management
Sorted Out with Attainable Region Analysis
Attainable Region
S,S&L Chapt. 7
Attainable Region
• Graphical method that is used to determine the entire space feasible concentrations
• Useful for identifying reactor configurations that will yield the optimal products
ProcedureStep 1: Construct a trajectory for a PFR from the feed point,
continuing to complete conversion or chemical equilibriumStep 2: When the PFR bounds a convex region, this constitutes a
candidate AR. The procedure terminates if the rate vectors outside the candidate AR do not point back into it.
Step 3: The PFR trajectory is expanded by linear arcs, representing mixing between the PFR effluent and the feed stream, extending the candidate AR.
Step 4: Construct a CSTR trajectory to see if the AR can be extended. Place linear arcs, which represent mixing, on the CSTR trajectory to ensure the trajectory remains convex.
Step 5: A PFR trajectory is drawn from the position where the mixing line meets the CSTR trajectory. If the PFR trajectory is convex, it extends the previous AR to form a expanded AR. Then return to step 2. Otherwise, repeat the procedure from Step 3.
Example
BBAB
ABAA
k
kk
k
CkCkCkdt
dC
CkCkCkdt
dC
DA
CdesiredBA
321
2421
4
31
2
2
)(
Reactions
Rate Equations
Step 1
Begin by constructing a trajectory for a PFR from the feed point, continuing to the complete conversion of A or chemical equilibrium
• Solve the PFR design equations numerically– Use the feed conditions as initial conditions to
the o.d.e.– Adjust integration range, (residence time),
until complete conversion or to equilibrium
PFR Design Equations
BBAB
ABAA
CkCkCkdt
dC
CkCkCkdt
dC
321
2421
x
AA r
dxFV
0
0
Solve NumericallyRunge-Kutta
Solve Numerically
Step 2Plot the PFR trajectory from the previous results. Check to see if rate vectors outside AR point back into it (e.g. Look for non-convex regions on the curve. Tangent line passing (1,0))
Des
ired
Step 3
Expand the AR as much as possible with straight arcs that represent mixing of reactor effluent and feed stream
PFR
(1-)
Interpreting points on mixing line
Larger Attainable Region
PFR CA=0. 2187CB=0.00011 CA=0.72
CB=0.00004
CA=1CB=0
(1-)PFR
CA=1CB=0
(1-)
Mixing of StreamsReactant Bypass
21 )1( ccc Vector Equation, i component is CA, j component is CB
α =fraction of mixture of stream 1in the mixed stream
)1(00011.0000004.0
)1(2187.0172.0
B
A
C
C
Feed mixing fraction: = 0. 64
Step 4
If a mixing arc extends the attainable region on a PFR trajectory, check to see if a CSTR trajectory can extend the attainable region
For CSTR trajectories that extend the attainable region, add mixing arcs to concave regions to ensure the attainable region remains convex
• Solve CSTR multiple NLE numerically– Vary until all feed is consumed or equilibrium is
reached
CSTR Design Equations
)(
)(
321
2421
BBAB
ABAAAo
CkCkCkC
CkCkCkCC
A
A
r
xFV
0
Solve numerically at various until complete conversion or
equilibrium is achieved
CSTR Extends Attainable Region
CSTR
Plot extends attainable regioni.c. for step 5
CSTR
CA=1CB=0
(1-)
EnlargesAttainableRegion
Possible Configuration at this point
CSTR
CA=1CB=0
βPFR
β = 0
β = 0
1-α-β
0.38
β = 1α = 0
Profit ($) = 15000*CB-15*CA2
Attainable Region
-0.00001
0.00001
0.00003
0.00005
0.00007
0.00009
0.00011
0.00013
0.00015
0 0.05 0.1 0.15 0.2 0.25 0.3 0.35 0.4 0.45 0.5 0.55 0.6 0.65 0.7 0.75 0.8 0.85 0.9 0.95 1 1.05
C a, kmol / m3
CSTR
PFR
PFR2
$=0.9
$=2
$=1.5
Optimal point not at highest selectivity
PFRCSTR
Conclusions
• Need to know feed conditions• AR graphical method is 2-D and limited to 2
independent species• Systems with rate expressions involving more
than 2 species need to be reduced– Atom balances are used to reduce independent
species– Independent species = #molecular species - #atomic
species• If independent species < 2, AR can be used by Principle of
Reaction Invariants
Separation Trains
S, S&L Chapt. 8
Separation Methods
• Absorption
• Stripping
• Distillation
• Membrane Separations
• Crystallization
• etc
Use of Separation Units
Column Sequences
• No. of Columns– Nc=P-1
• P= No. of Products
• No. of Possible Column Sequences– Ns=[2(P-1)]!/[P!(P-1)!]
• P= No. of Products
– P=3, Nc=2, Ns=2– P=4, Nc=3, Ns=5 – P=5, Nc=4, Ns=14– P=6, Nc=5, Ns=42– P=7, Nc=6, Ns=132
No. of Possible Column Sequences Blows up!
How do I evaluate which is best sequence?
Marginal Vapor Rate
• Marginal Annualized Cost~ Marginal Vapor Rate• Marginal Annualized Cost proportional to
– Reboiler Duty (Operating Cost)– Reboiler Area (Capital Cost)– Condenser Duty (Operating Cost)– Condenser Area (Capital Cost)– Diameter of Column (Capital Cost)
• Vapor Rate is proportional to all of the above
Selecting Multiple Column Separation Trains
• Minimum Cost for Separation Train will occur when you have a– Minimum of Total Vapor Flow Rate for all
columns– R= 1.2 Rmin – V=D (R+1)
• V= Vapor Flow Rate• D= Distillate Flow Rate• R=Recycle Ratio
Azeotrope Conditions
• Conditions on the Activity Coefficient
• Minimum Boiling, γjL> 1
• Maximum Boiling, γjL< 1
• xj=yj, j=,1,2,…C
Law sRaoult' from Deviations Negative,...2,1,1
Law sRaoult' from Deviations Positive,...2,1,1
)1( 221111
Cj
Cj
PxPxP
Lj
Lj
sLsLT
Raoult’s Law
satii PxP
Importance of Physical Property Data Set
• In all cases– Need sophisticated liquid phase model to
accurately predict the activity coefficient for the liquid.
• For High Pressure Cases Only– Also need sophisticated (non-ideal) gas
phase fugacity model
Multi-component Azeotropes
• Residue Curve Map– dxj /dť = dxj /d ln(L) = xj – yj
• Integrate from various starting points
Defining Conditions for Multi-component Azeotrope
t goes from 0 to 1, ideal to non-ideal to find Azeotrope
Distillation
• XB, XF and YD form a line for a Distillation Column
• Line can not cross Azeotrope line
Ethanol/Water Distillation with
BenzeneTo Break Azeotrope
Pressure Swing to Break Azeotrope
Temp. of Azeotropevs. Pressure
Mole Fraction of Azeotrope
Reactor-Separation Train-Recycle
Chapt. 7&8
Trade-off between Reactor and Separator
• Factors– Reactor Conversion of limiting reactant
• Effects cost and size of Separation Train
– Reactor Temperature and mode of operation (adiabatic, isothermal, etc.)
• Effect utility costs for separation and reaction• Effect impurities from side reactions
– High Reactor Pressure for Le Chatlier cases (less moles of product)
• Higher cost for recycle compression
Trade-off between Reactor and Separator
• Factors, cont.– Use of excess of one or more reactant to increase
equilibrium conversion and/or reaction rate• Increases cost of separation train
– Use of diluents in adiabatic reactor to control temperature in reactor
• Increases cost of separations train
– Use of purge to avoid difficult separation.• Decreases the cost of separations• Loss of reactants – increase cost of reactants• May increased cost of reactor, depending on the purge-to-
recycle ratio
Factors that effect recycle/purge
• Factor– Excess reactants
• Increases recycle flow• Increases separation costs
– Concentration of impurities to be purged• Effects the recycle-to-purge ratio
– Reactor outlet temperature and pressure• Increase cost of utilities in separation• Increase cost to recycle - compressor
Compare Recycle Concepts
• Costs
• Benefits
Feedback effects of Recycle Loop
• Small disturbance on feed
• Large effect on recycle flow rate/composition
• Snowball effect on reactor/separator
Heat Integration
Chapter 9
Terry Ring
University of Utah
Costs
• Heat Exchanger Purchase Cost– CP=K(Area)0.6
• Annual Cost– CA=im[ΣCp,i+ ΣCP,A,j]+sFs+(cw)Fcw
• im=return on investment• Fs= Annual Flow of Steam,
– $5.5/ston to $12.1/ston = s
• Fcw=Annual Flow of Cold Water– $0.013/ston = cw
Lost Work = Lost Money
• Transfer Heat from T1 to T2
• ΔT approach Temp. for Heat Exchanger
• To= Temperature of Environment
• Use 1st and 2nd laws of Thermodynamics
• LW=QToΔT/(T1T2)– ΔT=T1-T2
– To= Environment Temperature
• Q= UAΔTlm
T1
T2
Q
Heat Integration
• Make list of HX• Instead of using utilities can you use
another stream to heat/cool any streams?• How much of this can you do without
causing operational problems?• Can you use air to cool?
– Air is a low cost coolant.
• Less utilities = smaller cost of operations
Terms
• HEN=Heat Exchanger Network
• MER=Maximum Energy Recovery
• Minimum Number of Heat Exchangers
• Threshold Approach Temperature
• Optimum Approach Temperature
Process
Minimize UtilitiesFor 4 Streams
Adjust Hot Stream Temperatures to Give ΔTmin
Enthalpy Differences for Temperature Intervals
Pinch Analysis
Minimum Utilities
Pinch Analysis
ΔTapp
MER values
How to combine hot with cold?
• At Pinch (temp touching pinch)– Above Pinch Connect
• Cc≥Ch
– Below Pinch Connect• Ch≥Cc
• Not touching Pinch temp.– No requirement for Cc or Ch
4 Heat ExchangerHEN for Min. Utilities
Cc≥Ch
Ch≥Cc
MER Values
Stream Splitting
• Two streams created from one
• one heat exchanger on each piece of split stream with couplings
1
1a
1b
1b
1a
1
Optimization of HEN
• How does approach delta T (ΔTmin) effect the total cost of HEN?
• Q= UA ΔT
• LW=QToΔT/(T1T2)
– More Utility cost
Costs
• Heat Exchanger Purchase Cost– CP=K(Area)0.6
• Annual Cost– CA=im[ΣCp,i+ ΣCP,A,j]+sFs+(cw)Fcw
• im=return on investment• Fs= Annual Flow of Steam,
– $5.5/ston to $12.1/ston
• Fcw=Annual Flow of Cold Water– $0.013/ston
Change ΔTmin
CP=K(Area)0.6
Area=Q/(UF ΔTmin)
More Lost Work
LW=QToΔT/(T1T2)
Optimization of Process Flowsheets
Chapter 24
Terry A. Ring
CHEN 5353
Degrees of Freedom
• Over Specified Problem – Fitting Data– Nvariables>>Nequations
• Equally Specified Problem – Units in Flow sheet– Nvariables=Nequations
• Under Specified Problem– Optimization– Nvariables<<Nequations
Optimization
• Number of Decision Variables– ND=Nvariables-Nequations
• Objective Function is optimized with respect to ND
Variables– Minimize Cost– Maximize Investor Rate of Return
• Subject To Constraints– Equality Constraints
• Mole fractions add to 1
– Inequality Constraints• Reflux ratio is larger than Rmin
– Upper and Lower Bounds• Mole fraction is larger than zero and smaller than 1
PRACTICAL ASPECTS• Design variables, need to be identified and kept
free for manipulation by optimizer – e.g., in a distillation column, reflux ratio specification
and distillate flow specification are degrees of freedom, rather than their actual values themselves
• Design variables should be selected AFTER ensuring that the objective function is sensitive to their values– e.g., the capital cost of a given column may be
insensitive to the column feed temperature
• Do not use discrete-valued variables in gradient-based optimization as they lead to discontinuities in f(d)
Optimization
• Feasible Region– Unconstrained Optimization
• No constraints– Uni-modal– Multi-modal
– Constrained Optimization• Constraints
– Slack– Binding
v
v
v
N
i ii=1
i V
N
ij j i Ej=1
N
ij j i Ij=1
MinimizeJ x f xd
Subject to (s.t.) x 0,i 1, ,N
a x b,i 1, ,N
c x d,i 1, ,N
LINEAR PROGRAMING (LP)
equality constraints
inequality constraints
objective function
w.r.t. design variables The ND design variables, d, are adjusted to minimize f{x} while satisfying the constraints
EXAMPLE LP – GRAPHICAL SOLUTION
A refinery uses two crude oils, with yields as below.
Volumetric Yields Max. Production
Crude #1 Crude #2 (bbl/day)
Gasoline 70 31 6,000
Kerosene 6 9 2,400
Fuel Oil 24 60 12,000The profit on processing each crude is:
$2/bbl for Crude #1 and $1.4/bbl for Crude #2.
a) What is the optimum daily processing rate for each grade?
b) What is the optimum if 6,000 bbl/day of gasoline is needed?
EXAMPLE LP –SOLUTION (Cont’d)Step 1. Identify the variables. Let x1 and x2 be the daily production rates of Crude #1 and Crude #2.
Step 2. Select objective function. We need to maximizemaximize profit: 1 2J x 2.00x 1.40x
Step 3. Develop models for process and constraints. Only constraints on the three products are given:
Step 4. Simplification of model and objective function. Equality constraints are used to reduce the number of independent variables (ND = NV – NE). Here NE = 0.
1 2
1 2
1 2
0.70x 0.31x 6,000
0.06x 0.09x 2,400
0.24x 0.60x 12,000
EXAMPLE LP –SOLUTION (Cont’d)
Step 5. Compute optimum. a) Inequality constraints define feasible space.
1 20.70x 0.31x 6,000
1 20.06x 0.09x 2,400
1 20.24x 0.60x 12,000Feasible
Space
EXAMPLE LP –SOLUTION (Cont’d)Step 5. Compute optimum. b) Constant J contours are positioned to find
optimum.
J = 10,000
J = 20,000
J = 27,097
x1 = 0, x2 = 19,355 bbl/day
EXAMPLE LP – GRAPHICAL SOLUTION
A refinery uses two crude oils, with yields as below.
Volumetric Yields Max. Production
Crude #1 Crude #2 (bbl/day)
Gasoline 70 31 6,000
Kerosene 6 9 2,400
Fuel Oil 24 60 12,000The profit on processing each crude is:
$2/bbl for Crude #1 and $1.4/bbl for Crude #2.
a) What is the optimum daily processing rate for each grade?
b) What is the optimum if 6,000 bbl/day of gasoline is needed?
Dealing with Impurities in Processes and Process Simulators
ChEN 5253 Design IITerry A. Ring
There is not chapter in the book on this subject
Impurity Effects
• Heat Exchange
• Reactors
• Separation Systems
• Recycle Loops
Impurities in Heat Exchange
• Impurities effect heat capacity– Lower Cp
• Various options
– Raise Cp
• Increase H2
• Impurities effect the enthalpy of stream– Total heat of condensation is less due to
impurity– Total heat of vaporization is less due to
impurity
Impurities in Separation Trains
• Non-condensable Impurities– Build up in Distillation column – Big Trouble!!
• Condensable Impurities– Cause some products to be less pure
• May not meet product specifications• Can not sell this product – Big Trouble!!
– Rework cost– Waste it– Sell for lower price
Processes are tested for Impurity Tolerance
• Add light and heavy impurities to feed– Low concentration
• All impurities add to 0.1 % of feed• (may need to increase Tolerance in Simulation)
– Medium concentration• All impurities add to 1% of feed
– High concentration• All impurities add to 10% of feed
• Find out where impurities end up in process• Find out if process falls apart due to impurities
– What purges are required to return process to function.
Impurities in Separation Trains
• It is important to know where the impurites will accumulate in the train
• Which products will be polluted by which impurities– Is that acceptable for sale of product?
Purging Impurities
• Find the point in the process where the impurities have the highest concentration– Put Purge here
• Put a purge in almost all recycle loops
Plant-Wide Controllability
• Control Architecture– DoF analysis Dynamic
Analysis• No. of valves
– DoF analysis Steady State Analysis
• No. of valves – No. of liquid level loops
• Product Flow Control or Feed Flow Control
• Types of control– Single loop PID– Gain Scheduling– Ratio control– Cascade Control– Multi-variable control– Model Based control
(MPC)– Override control
Distillation Control
• Types of Control– LV control– DV– LB– DB– (L/D)(V/B)– (L/F)(V/F)
The End